Overview

Dataset statistics

Number of variables26
Number of observations38577
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory181.0 B

Variable types

Numeric10
Categorical16

Alerts

issue_d has a high cardinality: 55 distinct valuesHigh cardinality
earliest_cr_line has a high cardinality: 524 distinct valuesHigh cardinality
loan_amnt is highly overall correlated with funded_amnt_inv and 1 other fieldsHigh correlation
funded_amnt_inv is highly overall correlated with loan_amnt and 1 other fieldsHigh correlation
int_rate is highly overall correlated with grade and 2 other fieldsHigh correlation
installment is highly overall correlated with loan_amnt and 1 other fieldsHigh correlation
annual_inc is highly overall correlated with annual_inc_groupsHigh correlation
open_acc is highly overall correlated with total_acc and 1 other fieldsHigh correlation
revol_util is highly overall correlated with revol_util_groupsHigh correlation
total_acc is highly overall correlated with open_acc and 1 other fieldsHigh correlation
grade is highly overall correlated with int_rate and 2 other fieldsHigh correlation
sub_grade is highly overall correlated with int_rate and 2 other fieldsHigh correlation
int_rate_groups is highly overall correlated with int_rate and 2 other fieldsHigh correlation
open_acc_groups is highly overall correlated with open_accHigh correlation
revol_util_groups is highly overall correlated with revol_utilHigh correlation
total_acc_groups is highly overall correlated with total_accHigh correlation
annual_inc_groups is highly overall correlated with annual_incHigh correlation
pub_rec is highly imbalanced (86.5%)Imbalance
open_acc_groups is highly imbalanced (52.8%)Imbalance
annual_inc_groups is highly imbalanced (99.8%)Imbalance
annual_inc is highly skewed (γ1 = 31.19841374)Skewed
inq_last_6mths has 18709 (48.5%) zerosZeros
revol_util has 1004 (2.6%) zerosZeros

Reproduction

Analysis started2023-05-10 17:59:11.751861
Analysis finished2023-05-10 17:59:51.155840
Duration39.4 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

loan_amnt
Real number (ℝ)

Distinct870
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11047.025
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:51.701453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15300
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9700

Descriptive statistics

Standard deviation7348.4416
Coefficient of variation (CV)0.66519641
Kurtosis0.84295245
Mean11047.025
Median Absolute Deviation (MAD)4600
Skewness1.0781027
Sum4.261611 × 108
Variance53999595
MonotonicityNot monotonic
2023-05-10T23:29:51.976554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2809
 
7.3%
12000 2248
 
5.8%
5000 2028
 
5.3%
6000 1886
 
4.9%
15000 1838
 
4.8%
8000 1568
 
4.1%
20000 1536
 
4.0%
25000 1327
 
3.4%
4000 1123
 
2.9%
3000 1018
 
2.6%
Other values (860) 21196
54.9%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 298
0.8%
1050 4
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 601
1.6%
34800 2
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475 5
 
< 0.1%
34200 1
 
< 0.1%
34000 13
 
< 0.1%
33950 8
 
< 0.1%
33600 4
 
< 0.1%
33500 2
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

Distinct8050
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10222.481
Minimum0
Maximum35000
Zeros129
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:52.307563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1800
Q15000
median8733.44
Q314000
95-th percentile24500.067
Maximum35000
Range35000
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation7022.7206
Coefficient of variation (CV)0.68698788
Kurtosis1.1648002
Mean10222.481
Median Absolute Deviation (MAD)4066.56
Skewness1.1299968
Sum3.9435265 × 108
Variance49318605
MonotonicityNot monotonic
2023-05-10T23:29:52.576981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1294
 
3.4%
10000 1264
 
3.3%
6000 1182
 
3.1%
12000 1016
 
2.6%
8000 887
 
2.3%
4000 806
 
2.1%
3000 790
 
2.0%
15000 630
 
1.6%
7000 596
 
1.5%
2000 448
 
1.2%
Other values (8040) 29664
76.9%
ValueCountFrequency (%)
0 129
0.3%
0.000121098 1
 
< 0.1%
0.000531133 1
 
< 0.1%
0.000654607 1
 
< 0.1%
0.001867696 1
 
< 0.1%
0.001963093 1
 
< 0.1%
0.001966974 1
 
< 0.1%
0.002251738 1
 
< 0.1%
0.002283598 1
 
< 0.1%
0.002373058 1
 
< 0.1%
ValueCountFrequency (%)
35000 127
0.3%
34997.35245 1
 
< 0.1%
34993.65539 1
 
< 0.1%
34993.32571 1
 
< 0.1%
34993.26306 1
 
< 0.1%
34993.19696 1
 
< 0.1%
34990.4308 1
 
< 0.1%
34987.27101 1
 
< 0.1%
34977.34674 1
 
< 0.1%
34975.81636 1
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
36 months
29096 
60 months
9481 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters385770
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 29096
75.4%
60 months 9481
 
24.6%

Length

2023-05-10T23:29:52.818467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:29:53.042501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
months 38577
50.0%
36 29096
37.7%
60 9481
 
12.3%

Most occurring characters

ValueCountFrequency (%)
77154
20.0%
6 38577
10.0%
m 38577
10.0%
o 38577
10.0%
n 38577
10.0%
t 38577
10.0%
h 38577
10.0%
s 38577
10.0%
3 29096
 
7.5%
0 9481
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 231462
60.0%
Space Separator 77154
 
20.0%
Decimal Number 77154
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 38577
16.7%
o 38577
16.7%
n 38577
16.7%
t 38577
16.7%
h 38577
16.7%
s 38577
16.7%
Decimal Number
ValueCountFrequency (%)
6 38577
50.0%
3 29096
37.7%
0 9481
 
12.3%
Space Separator
ValueCountFrequency (%)
77154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231462
60.0%
Common 154308
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 38577
16.7%
o 38577
16.7%
n 38577
16.7%
t 38577
16.7%
h 38577
16.7%
s 38577
16.7%
Common
ValueCountFrequency (%)
77154
50.0%
6 38577
25.0%
3 29096
 
18.9%
0 9481
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77154
20.0%
6 38577
10.0%
m 38577
10.0%
o 38577
10.0%
n 38577
10.0%
t 38577
10.0%
h 38577
10.0%
s 38577
10.0%
3 29096
 
7.5%
0 9481
 
2.5%

int_rate
Real number (ℝ)

Distinct370
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.932219
Minimum5.42
Maximum24.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:53.276976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.94
median11.71
Q314.38
95-th percentile18.312
Maximum24.4
Range18.98
Interquartile range (IQR)5.44

Descriptive statistics

Standard deviation3.6913274
Coefficient of variation (CV)0.30935801
Kurtosis-0.44728002
Mean11.932219
Median Absolute Deviation (MAD)2.75
Skewness0.29362684
Sum460309.2
Variance13.625898
MonotonicityNot monotonic
2023-05-10T23:29:53.547042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.99 913
 
2.4%
11.49 790
 
2.0%
7.51 787
 
2.0%
13.49 749
 
1.9%
7.88 725
 
1.9%
7.49 651
 
1.7%
9.99 590
 
1.5%
7.9 574
 
1.5%
5.42 573
 
1.5%
11.71 559
 
1.4%
Other values (360) 31666
82.1%
ValueCountFrequency (%)
5.42 573
1.5%
5.79 410
1.1%
5.99 347
0.9%
6 16
 
< 0.1%
6.03 447
1.2%
6.17 252
0.7%
6.39 58
 
0.2%
6.54 305
0.8%
6.62 396
1.0%
6.76 168
 
0.4%
ValueCountFrequency (%)
24.4 1
 
< 0.1%
24.11 3
 
< 0.1%
23.91 9
< 0.1%
23.59 4
 
< 0.1%
23.52 6
 
< 0.1%
23.22 9
< 0.1%
23.13 8
< 0.1%
22.94 1
 
< 0.1%
22.85 8
< 0.1%
22.74 15
< 0.1%

installment
Real number (ℝ)

Distinct15022
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.46632
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:54.096833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile70.61
Q1165.74
median277.86
Q3425.55
95-th percentile760.82
Maximum1305.19
Range1289.5
Interquartile range (IQR)259.81

Descriptive statistics

Standard deviation208.63921
Coefficient of variation (CV)0.64701087
Kurtosis1.3179975
Mean322.46632
Median Absolute Deviation (MAD)121.76
Skewness1.1504865
Sum12439783
Variance43530.322
MonotonicityNot monotonic
2023-05-10T23:29:54.363416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.11 68
 
0.2%
180.96 59
 
0.2%
311.02 54
 
0.1%
150.8 48
 
0.1%
368.45 46
 
0.1%
372.12 45
 
0.1%
330.76 43
 
0.1%
339.31 42
 
0.1%
317.72 41
 
0.1%
301.6 41
 
0.1%
Other values (15012) 38090
98.7%
ValueCountFrequency (%)
15.69 1
< 0.1%
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
19.87 1
< 0.1%
20.22 1
< 0.1%
21.25 1
< 0.1%
21.81 1
< 0.1%
22.51 1
< 0.1%
ValueCountFrequency (%)
1305.19 1
 
< 0.1%
1302.69 1
 
< 0.1%
1295.21 1
 
< 0.1%
1288.1 2
 
< 0.1%
1283.5 1
 
< 0.1%
1276.6 3
< 0.1%
1272.2 1
 
< 0.1%
1269.73 5
< 0.1%
1265.16 1
 
< 0.1%
1263.23 1
 
< 0.1%

grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
B
11675 
A
10045 
C
7834 
D
5085 
E
2663 
Other values (2)
1275 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38577
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowA

Common Values

ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Length

2023-05-10T23:29:54.605731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:29:54.829881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
b 11675
30.3%
a 10045
26.0%
c 7834
20.3%
d 5085
13.2%
e 2663
 
6.9%
f 976
 
2.5%
g 299
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38577
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 38577
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

sub_grade
Categorical

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
A4
2873 
B3
2825 
A5
2715 
B5
2615 
B4
 
2437
Other values (30)
25112 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77154
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowA4

Common Values

ValueCountFrequency (%)
A4 2873
 
7.4%
B3 2825
 
7.3%
A5 2715
 
7.0%
B5 2615
 
6.8%
B4 2437
 
6.3%
C1 2055
 
5.3%
B2 2001
 
5.2%
C2 1931
 
5.0%
A3 1810
 
4.7%
B1 1797
 
4.7%
Other values (25) 15518
40.2%

Length

2023-05-10T23:29:55.055889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4 2873
 
7.4%
b3 2825
 
7.3%
a5 2715
 
7.0%
b5 2615
 
6.8%
b4 2437
 
6.3%
c1 2055
 
5.3%
b2 2001
 
5.2%
c2 1931
 
5.0%
a3 1810
 
4.7%
b1 1797
 
4.7%
Other values (25) 15518
40.2%

Most occurring characters

ValueCountFrequency (%)
B 11675
15.1%
A 10045
13.0%
4 8063
10.5%
3 7974
10.3%
5 7847
10.2%
C 7834
10.2%
2 7650
9.9%
1 7043
9.1%
D 5085
6.6%
E 2663
 
3.5%
Other values (2) 1275
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38577
50.0%
Decimal Number 38577
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 8063
20.9%
3 7974
20.7%
5 7847
20.3%
2 7650
19.8%
1 7043
18.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 38577
50.0%
Common 38577
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%
Common
ValueCountFrequency (%)
4 8063
20.9%
3 7974
20.7%
5 7847
20.3%
2 7650
19.8%
1 7043
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 11675
15.1%
A 10045
13.0%
4 8063
10.5%
3 7974
10.3%
5 7847
10.2%
C 7834
10.2%
2 7650
9.9%
1 7043
9.1%
D 5085
6.6%
E 2663
 
3.5%
Other values (2) 1275
 
1.7%

emp_length
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
10+ years
9521 
< 1 year
4508 
2 years
4291 
3 years
4012 
4 years
3342 
Other values (6)
12903 

Length

Max length9
Median length7
Mean length7.52832
Min length6

Characters and Unicode

Total characters290420
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row3 years

Common Values

ValueCountFrequency (%)
10+ years 9521
24.7%
< 1 year 4508
11.7%
2 years 4291
11.1%
3 years 4012
10.4%
4 years 3342
 
8.7%
5 years 3194
 
8.3%
1 year 3169
 
8.2%
6 years 2168
 
5.6%
7 years 1711
 
4.4%
8 years 1435
 
3.7%

Length

2023-05-10T23:29:55.256324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 30900
37.8%
10 9521
 
11.7%
1 7677
 
9.4%
year 7677
 
9.4%
4508
 
5.5%
2 4291
 
5.3%
3 4012
 
4.9%
4 3342
 
4.1%
5 3194
 
3.9%
6 2168
 
2.7%
Other values (3) 4372
 
5.4%

Most occurring characters

ValueCountFrequency (%)
43085
14.8%
y 38577
13.3%
e 38577
13.3%
a 38577
13.3%
r 38577
13.3%
s 30900
10.6%
1 17198
 
5.9%
0 9521
 
3.3%
+ 9521
 
3.3%
< 4508
 
1.6%
Other values (8) 21379
7.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 185208
63.8%
Decimal Number 48098
 
16.6%
Space Separator 43085
 
14.8%
Math Symbol 14029
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17198
35.8%
0 9521
19.8%
2 4291
 
8.9%
3 4012
 
8.3%
4 3342
 
6.9%
5 3194
 
6.6%
6 2168
 
4.5%
7 1711
 
3.6%
8 1435
 
3.0%
9 1226
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
y 38577
20.8%
e 38577
20.8%
a 38577
20.8%
r 38577
20.8%
s 30900
16.7%
Math Symbol
ValueCountFrequency (%)
+ 9521
67.9%
< 4508
32.1%
Space Separator
ValueCountFrequency (%)
43085
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 185208
63.8%
Common 105212
36.2%

Most frequent character per script

Common
ValueCountFrequency (%)
43085
41.0%
1 17198
 
16.3%
0 9521
 
9.0%
+ 9521
 
9.0%
< 4508
 
4.3%
2 4291
 
4.1%
3 4012
 
3.8%
4 3342
 
3.2%
5 3194
 
3.0%
6 2168
 
2.1%
Other values (3) 4372
 
4.2%
Latin
ValueCountFrequency (%)
y 38577
20.8%
e 38577
20.8%
a 38577
20.8%
r 38577
20.8%
s 30900
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 290420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43085
14.8%
y 38577
13.3%
e 38577
13.3%
a 38577
13.3%
r 38577
13.3%
s 30900
10.6%
1 17198
 
5.9%
0 9521
 
3.3%
+ 9521
 
3.3%
< 4508
 
1.6%
Other values (8) 21379
7.4%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
RENT
18480 
MORTGAGE
17021 
OWN
2975 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length5
Mean length5.6903077
Min length3

Characters and Unicode

Total characters219515
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 18480
47.9%
MORTGAGE 17021
44.1%
OWN 2975
 
7.7%
OTHER 98
 
0.3%
NONE 3
 
< 0.1%

Length

2023-05-10T23:29:55.502320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:29:55.715546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
rent 18480
47.9%
mortgage 17021
44.1%
own 2975
 
7.7%
other 98
 
0.3%
none 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 219515
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 219515
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

annual_inc
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5215
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68777.974
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:55.940791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140000
median58868
Q382000
95-th percentile140004
Maximum6000000
Range5996000
Interquartile range (IQR)42000

Descriptive statistics

Standard deviation64218.682
Coefficient of variation (CV)0.93371
Kurtosis2308.7752
Mean68777.974
Median Absolute Deviation (MAD)19868
Skewness31.198414
Sum2.6532479 × 109
Variance4.1240391 × 109
MonotonicityNot monotonic
2023-05-10T23:29:56.197260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1466
 
3.8%
50000 1029
 
2.7%
40000 855
 
2.2%
45000 811
 
2.1%
30000 808
 
2.1%
75000 786
 
2.0%
65000 779
 
2.0%
70000 714
 
1.9%
48000 696
 
1.8%
80000 636
 
1.6%
Other values (5205) 29997
77.8%
ValueCountFrequency (%)
4000 1
 
< 0.1%
4080 1
 
< 0.1%
4200 2
 
< 0.1%
4800 4
< 0.1%
4888 1
 
< 0.1%
5000 1
 
< 0.1%
5500 1
 
< 0.1%
6000 5
< 0.1%
7000 1
 
< 0.1%
7200 4
< 0.1%
ValueCountFrequency (%)
6000000 1
 
< 0.1%
3900000 1
 
< 0.1%
2039784 1
 
< 0.1%
1900000 1
 
< 0.1%
1782000 1
 
< 0.1%
1440000 1
 
< 0.1%
1362000 1
 
< 0.1%
1250000 1
 
< 0.1%
1200000 4
< 0.1%
1176000 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Not Verified
16694 
Verified
12206 
Source Verified
9677 

Length

Max length15
Median length12
Mean length11.486922
Min length8

Characters and Unicode

Total characters443131
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Not Verified 16694
43.3%
Verified 12206
31.6%
Source Verified 9677
25.1%

Length

2023-05-10T23:29:56.447046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:29:56.666126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
verified 38577
59.4%
not 16694
25.7%
source 9677
 
14.9%

Most occurring characters

ValueCountFrequency (%)
e 86831
19.6%
i 77154
17.4%
r 48254
10.9%
V 38577
8.7%
f 38577
8.7%
d 38577
8.7%
o 26371
 
6.0%
26371
 
6.0%
N 16694
 
3.8%
t 16694
 
3.8%
Other values (3) 29031
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 351812
79.4%
Uppercase Letter 64948
 
14.7%
Space Separator 26371
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 86831
24.7%
i 77154
21.9%
r 48254
13.7%
f 38577
11.0%
d 38577
11.0%
o 26371
 
7.5%
t 16694
 
4.7%
u 9677
 
2.8%
c 9677
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V 38577
59.4%
N 16694
25.7%
S 9677
 
14.9%
Space Separator
ValueCountFrequency (%)
26371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 416760
94.0%
Common 26371
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 86831
20.8%
i 77154
18.5%
r 48254
11.6%
V 38577
9.3%
f 38577
9.3%
d 38577
9.3%
o 26371
 
6.3%
N 16694
 
4.0%
t 16694
 
4.0%
S 9677
 
2.3%
Other values (2) 19354
 
4.6%
Common
ValueCountFrequency (%)
26371
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 443131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 86831
19.6%
i 77154
17.4%
r 48254
10.9%
V 38577
8.7%
f 38577
8.7%
d 38577
8.7%
o 26371
 
6.0%
26371
 
6.0%
N 16694
 
3.8%
t 16694
 
3.8%
Other values (3) 29031
 
6.6%

issue_d
Categorical

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Nov-11
 
2062
Dec-11
 
2042
Oct-11
 
1941
Sep-11
 
1913
Aug-11
 
1798
Other values (50)
28821 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters231462
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDec-11
2nd rowDec-11
3rd rowDec-11
4th rowDec-11
5th rowDec-11

Common Values

ValueCountFrequency (%)
Nov-11 2062
 
5.3%
Dec-11 2042
 
5.3%
Oct-11 1941
 
5.0%
Sep-11 1913
 
5.0%
Aug-11 1798
 
4.7%
Jul-11 1745
 
4.5%
Jun-11 1728
 
4.5%
May-11 1609
 
4.2%
Apr-11 1559
 
4.0%
Mar-11 1442
 
3.7%
Other values (45) 20738
53.8%

Length

2023-05-10T23:29:56.855748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nov-11 2062
 
5.3%
dec-11 2042
 
5.3%
oct-11 1941
 
5.0%
sep-11 1913
 
5.0%
aug-11 1798
 
4.7%
jul-11 1745
 
4.5%
jun-11 1728
 
4.5%
may-11 1609
 
4.2%
apr-11 1559
 
4.0%
mar-11 1442
 
3.7%
Other values (45) 20738
53.8%

Most occurring characters

ValueCountFrequency (%)
1 52564
22.7%
- 38577
16.7%
0 18061
 
7.8%
e 10071
 
4.4%
u 9919
 
4.3%
J 8910
 
3.8%
a 7989
 
3.5%
c 7976
 
3.4%
p 6329
 
2.7%
A 6219
 
2.7%
Other values (18) 64847
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77154
33.3%
Lowercase Letter 77154
33.3%
Dash Punctuation 38577
16.7%
Uppercase Letter 38577
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10071
13.1%
u 9919
12.9%
a 7989
10.4%
c 7976
10.3%
p 6329
8.2%
n 5559
7.2%
r 5522
7.2%
o 4006
 
5.2%
v 4006
 
5.2%
t 3761
 
4.9%
Other values (4) 12016
15.6%
Uppercase Letter
ValueCountFrequency (%)
J 8910
23.1%
A 6219
16.1%
M 5610
14.5%
D 4215
10.9%
N 4006
10.4%
O 3761
9.7%
S 3498
 
9.1%
F 2358
 
6.1%
Decimal Number
ValueCountFrequency (%)
1 52564
68.1%
0 18061
 
23.4%
9 4716
 
6.1%
8 1562
 
2.0%
7 251
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115731
50.0%
Latin 115731
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10071
 
8.7%
u 9919
 
8.6%
J 8910
 
7.7%
a 7989
 
6.9%
c 7976
 
6.9%
p 6329
 
5.5%
A 6219
 
5.4%
M 5610
 
4.8%
n 5559
 
4.8%
r 5522
 
4.8%
Other values (12) 41627
36.0%
Common
ValueCountFrequency (%)
1 52564
45.4%
- 38577
33.3%
0 18061
 
15.6%
9 4716
 
4.1%
8 1562
 
1.3%
7 251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 52564
22.7%
- 38577
16.7%
0 18061
 
7.8%
e 10071
 
4.4%
u 9919
 
4.3%
J 8910
 
3.8%
a 7989
 
3.5%
c 7976
 
3.4%
p 6329
 
2.7%
A 6219
 
2.7%
Other values (18) 64847
28.0%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Fully Paid
32950 
Charged Off
5627 

Length

Max length11
Median length10
Mean length10.145864
Min length10

Characters and Unicode

Total characters391397
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 32950
85.4%
Charged Off 5627
 
14.6%

Length

2023-05-10T23:29:57.042650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:29:57.256816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
fully 32950
42.7%
paid 32950
42.7%
charged 5627
 
7.3%
off 5627
 
7.3%

Most occurring characters

ValueCountFrequency (%)
l 65900
16.8%
38577
9.9%
a 38577
9.9%
d 38577
9.9%
F 32950
8.4%
u 32950
8.4%
y 32950
8.4%
P 32950
8.4%
i 32950
8.4%
f 11254
 
2.9%
Other values (6) 33762
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 275666
70.4%
Uppercase Letter 77154
 
19.7%
Space Separator 38577
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 65900
23.9%
a 38577
14.0%
d 38577
14.0%
u 32950
12.0%
y 32950
12.0%
i 32950
12.0%
f 11254
 
4.1%
h 5627
 
2.0%
r 5627
 
2.0%
g 5627
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
F 32950
42.7%
P 32950
42.7%
C 5627
 
7.3%
O 5627
 
7.3%
Space Separator
ValueCountFrequency (%)
38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 352820
90.1%
Common 38577
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 65900
18.7%
a 38577
10.9%
d 38577
10.9%
F 32950
9.3%
u 32950
9.3%
y 32950
9.3%
P 32950
9.3%
i 32950
9.3%
f 11254
 
3.2%
C 5627
 
1.6%
Other values (5) 28135
8.0%
Common
ValueCountFrequency (%)
38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 391397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 65900
16.8%
38577
9.9%
a 38577
9.9%
d 38577
9.9%
F 32950
8.4%
u 32950
8.4%
y 32950
8.4%
P 32950
8.4%
i 32950
8.4%
f 11254
 
2.9%
Other values (6) 33762
8.6%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
debt_consolidation
18055 
credit_card
5027 
other
3865 
home_improvement
2875 
major_purchase
2150 
Other values (9)
6605 

Length

Max length18
Median length16
Mean length13.726106
Min length3

Characters and Unicode

Total characters529512
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowwedding

Common Values

ValueCountFrequency (%)
debt_consolidation 18055
46.8%
credit_card 5027
 
13.0%
other 3865
 
10.0%
home_improvement 2875
 
7.5%
major_purchase 2150
 
5.6%
small_business 1754
 
4.5%
car 1499
 
3.9%
wedding 926
 
2.4%
medical 681
 
1.8%
moving 576
 
1.5%
Other values (4) 1169
 
3.0%

Length

2023-05-10T23:29:57.461097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 18055
46.8%
credit_card 5027
 
13.0%
other 3865
 
10.0%
home_improvement 2875
 
7.5%
major_purchase 2150
 
5.6%
small_business 1754
 
4.5%
car 1499
 
3.9%
wedding 926
 
2.4%
medical 681
 
1.8%
moving 576
 
1.5%
Other values (4) 1169
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o 67573
12.8%
d 49022
9.3%
i 48649
9.2%
t 48577
9.2%
n 43145
8.1%
e 42285
 
8.0%
c 33139
 
6.3%
a 32818
 
6.2%
_ 29963
 
5.7%
s 27588
 
5.2%
Other values (12) 106753
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 499549
94.3%
Connector Punctuation 29963
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 67573
13.5%
d 49022
9.8%
i 48649
9.7%
t 48577
9.7%
n 43145
8.6%
e 42285
8.5%
c 33139
 
6.6%
a 32818
 
6.6%
s 27588
 
5.5%
r 22797
 
4.6%
Other values (11) 83956
16.8%
Connector Punctuation
ValueCountFrequency (%)
_ 29963
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 499549
94.3%
Common 29963
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 67573
13.5%
d 49022
9.8%
i 48649
9.7%
t 48577
9.7%
n 43145
8.6%
e 42285
8.5%
c 33139
 
6.6%
a 32818
 
6.6%
s 27588
 
5.5%
r 22797
 
4.6%
Other values (11) 83956
16.8%
Common
ValueCountFrequency (%)
_ 29963
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 529512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 67573
12.8%
d 49022
9.3%
i 48649
9.2%
t 48577
9.2%
n 43145
8.1%
e 42285
 
8.0%
c 33139
 
6.3%
a 32818
 
6.2%
_ 29963
 
5.7%
s 27588
 
5.2%
Other values (12) 106753
20.2%

dti
Real number (ℝ)

Distinct2853
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.272727
Minimum0
Maximum29.99
Zeros178
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:57.700320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1
Q18.13
median13.37
Q318.56
95-th percentile23.8
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.6730443
Coefficient of variation (CV)0.50276362
Kurtosis-0.85629801
Mean13.272727
Median Absolute Deviation (MAD)5.21
Skewness-0.026842422
Sum512021.99
Variance44.52952
MonotonicityNot monotonic
2023-05-10T23:29:57.954445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178
 
0.5%
12 46
 
0.1%
18 45
 
0.1%
19.2 39
 
0.1%
13.2 39
 
0.1%
12.48 37
 
0.1%
16.8 37
 
0.1%
15 36
 
0.1%
6 36
 
0.1%
13.5 36
 
0.1%
Other values (2843) 38048
98.6%
ValueCountFrequency (%)
0 178
0.5%
0.01 3
 
< 0.1%
0.02 5
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
 
< 0.1%
0.08 5
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
29.99 1
 
< 0.1%
29.93 3
< 0.1%
29.92 2
< 0.1%
29.89 1
 
< 0.1%
29.88 1
 
< 0.1%
29.86 2
< 0.1%
29.85 1
 
< 0.1%
29.82 1
 
< 0.1%
29.79 1
 
< 0.1%
29.78 1
 
< 0.1%

earliest_cr_line
Categorical

Distinct524
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Oct-99
 
360
Nov-98
 
357
Oct-00
 
341
Dec-98
 
340
Dec-97
 
318
Other values (519)
36861 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters231462
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.1%

Sample

1st rowJan-85
2nd rowApr-99
3rd rowNov-01
4th rowFeb-96
5th rowNov-04

Common Values

ValueCountFrequency (%)
Oct-99 360
 
0.9%
Nov-98 357
 
0.9%
Oct-00 341
 
0.9%
Dec-98 340
 
0.9%
Dec-97 318
 
0.8%
Nov-99 315
 
0.8%
Nov-00 312
 
0.8%
Sep-00 299
 
0.8%
Oct-98 295
 
0.8%
Nov-97 293
 
0.8%
Other values (514) 35347
91.6%

Length

2023-05-10T23:29:58.176678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oct-99 360
 
0.9%
nov-98 357
 
0.9%
oct-00 341
 
0.9%
dec-98 340
 
0.9%
dec-97 318
 
0.8%
nov-99 315
 
0.8%
nov-00 312
 
0.8%
sep-00 299
 
0.8%
oct-98 295
 
0.8%
nov-97 293
 
0.8%
Other values (514) 35347
91.6%

Most occurring characters

ValueCountFrequency (%)
- 38577
16.7%
9 22648
 
9.8%
0 18912
 
8.2%
e 10239
 
4.4%
J 9161
 
4.0%
u 9043
 
3.9%
a 8871
 
3.8%
8 8153
 
3.5%
c 7918
 
3.4%
n 6194
 
2.7%
Other values (23) 91746
39.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77154
33.3%
Lowercase Letter 77154
33.3%
Dash Punctuation 38577
16.7%
Uppercase Letter 38577
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10239
13.3%
u 9043
11.7%
a 8871
11.5%
c 7918
10.3%
n 6194
8.0%
p 6151
8.0%
r 5361
6.9%
t 3967
 
5.1%
v 3811
 
4.9%
o 3811
 
4.9%
Other values (4) 11788
15.3%
Decimal Number
ValueCountFrequency (%)
9 22648
29.4%
0 18912
24.5%
8 8153
 
10.6%
7 4675
 
6.1%
4 4141
 
5.4%
5 4080
 
5.3%
6 4056
 
5.3%
3 3666
 
4.8%
1 3635
 
4.7%
2 3188
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
J 9161
23.7%
A 5859
15.2%
M 5540
14.4%
O 3967
10.3%
D 3951
10.2%
N 3811
9.9%
S 3505
 
9.1%
F 2783
 
7.2%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115731
50.0%
Latin 115731
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10239
 
8.8%
J 9161
 
7.9%
u 9043
 
7.8%
a 8871
 
7.7%
c 7918
 
6.8%
n 6194
 
5.4%
p 6151
 
5.3%
A 5859
 
5.1%
M 5540
 
4.8%
r 5361
 
4.6%
Other values (12) 41394
35.8%
Common
ValueCountFrequency (%)
- 38577
33.3%
9 22648
19.6%
0 18912
16.3%
8 8153
 
7.0%
7 4675
 
4.0%
4 4141
 
3.6%
5 4080
 
3.5%
6 4056
 
3.5%
3 3666
 
3.2%
1 3635
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 38577
16.7%
9 22648
 
9.8%
0 18912
 
8.2%
e 10239
 
4.4%
J 9161
 
4.0%
u 9043
 
3.9%
a 8871
 
3.8%
8 8153
 
3.5%
c 7918
 
3.4%
n 6194
 
2.7%
Other values (23) 91746
39.6%

inq_last_6mths
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87173705
Minimum0
Maximum8
Zeros18709
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:58.344842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0715457
Coefficient of variation (CV)1.2292075
Kurtosis2.524438
Mean0.87173705
Median Absolute Deviation (MAD)1
Skewness1.3843898
Sum33629
Variance1.1482102
MonotonicityNot monotonic
2023-05-10T23:29:58.537346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 18709
48.5%
1 10660
27.6%
2 5654
 
14.7%
3 2983
 
7.7%
4 316
 
0.8%
5 144
 
0.4%
6 63
 
0.2%
7 34
 
0.1%
8 14
 
< 0.1%
ValueCountFrequency (%)
0 18709
48.5%
1 10660
27.6%
2 5654
 
14.7%
3 2983
 
7.7%
4 316
 
0.8%
5 144
 
0.4%
6 63
 
0.2%
7 34
 
0.1%
8 14
 
< 0.1%
ValueCountFrequency (%)
8 14
 
< 0.1%
7 34
 
0.1%
6 63
 
0.2%
5 144
 
0.4%
4 316
 
0.8%
3 2983
 
7.7%
2 5654
 
14.7%
1 10660
27.6%
0 18709
48.5%

open_acc
Real number (ℝ)

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2754232
Minimum2
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:58.754150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q16
median9
Q312
95-th percentile17
Maximum44
Range42
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.4015883
Coefficient of variation (CV)0.47454312
Kurtosis1.6939605
Mean9.2754232
Median Absolute Deviation (MAD)3
Skewness1.0072882
Sum357818
Variance19.373979
MonotonicityNot monotonic
2023-05-10T23:29:58.972981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7 3909
10.1%
6 3846
10.0%
8 3814
9.9%
9 3607
9.4%
5 3111
 
8.1%
10 3097
 
8.0%
11 2666
 
6.9%
4 2300
 
6.0%
12 2198
 
5.7%
13 1855
 
4.8%
Other values (30) 8174
21.2%
ValueCountFrequency (%)
2 596
 
1.5%
3 1470
 
3.8%
4 2300
6.0%
5 3111
8.1%
6 3846
10.0%
7 3909
10.1%
8 3814
9.9%
9 3607
9.4%
10 3097
8.0%
11 2666
6.9%
ValueCountFrequency (%)
44 1
 
< 0.1%
42 1
 
< 0.1%
41 1
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 2
 
< 0.1%
35 4
< 0.1%
34 5
< 0.1%
33 3
< 0.1%
32 3
< 0.1%

pub_rec
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
0
36507 
1
 
2013
2
 
48
3
 
7
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38577
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36507
94.6%
1 2013
 
5.2%
2 48
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Length

2023-05-10T23:29:59.218327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:29:59.464030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 36507
94.6%
1 2013
 
5.2%
2 48
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 36507
94.6%
1 2013
 
5.2%
2 48
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38577
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36507
94.6%
1 2013
 
5.2%
2 48
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 38577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36507
94.6%
1 2013
 
5.2%
2 48
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36507
94.6%
1 2013
 
5.2%
2 48
 
0.1%
3 7
 
< 0.1%
4 2
 
< 0.1%

revol_util
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1088
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.639653
Minimum0
Maximum99.9
Zeros1004
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:29:59.734221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q125.1
median49
Q372.2
95-th percentile93.5
Maximum99.9
Range99.9
Interquartile range (IQR)47.1

Descriptive statistics

Standard deviation28.40046
Coefficient of variation (CV)0.58389521
Kurtosis-1.1093555
Mean48.639653
Median Absolute Deviation (MAD)23.5
Skewness-0.027297523
Sum1876371.9
Variance806.58613
MonotonicityNot monotonic
2023-05-10T23:30:00.018469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1004
 
2.6%
0.2 62
 
0.2%
63 62
 
0.2%
40.7 57
 
0.1%
31.2 57
 
0.1%
70.4 56
 
0.1%
61 56
 
0.1%
66.7 56
 
0.1%
57.4 55
 
0.1%
37.6 55
 
0.1%
Other values (1078) 37057
96.1%
ValueCountFrequency (%)
0 1004
2.6%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 1
 
< 0.1%
0.1 55
 
0.1%
0.12 1
 
< 0.1%
0.16 1
 
< 0.1%
0.2 62
 
0.2%
0.3 42
 
0.1%
ValueCountFrequency (%)
99.9 25
0.1%
99.8 23
0.1%
99.7 30
0.1%
99.6 22
0.1%
99.5 23
0.1%
99.4 21
0.1%
99.3 29
0.1%
99.2 17
< 0.1%
99.1 28
0.1%
99 32
0.1%

total_acc
Real number (ℝ)

Distinct82
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.052648
Minimum2
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-05-10T23:30:00.300457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median20
Q329
95-th percentile43
Maximum90
Range88
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.425861
Coefficient of variation (CV)0.5181174
Kurtosis0.70052296
Mean22.052648
Median Absolute Deviation (MAD)7
Skewness0.83248058
Sum850725
Variance130.55029
MonotonicityNot monotonic
2023-05-10T23:30:00.568417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1424
 
3.7%
15 1418
 
3.7%
17 1411
 
3.7%
14 1403
 
3.6%
20 1390
 
3.6%
18 1379
 
3.6%
13 1366
 
3.5%
21 1356
 
3.5%
19 1303
 
3.4%
12 1291
 
3.3%
Other values (72) 24836
64.4%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 179
 
0.5%
4 415
 
1.1%
5 543
1.4%
6 674
1.7%
7 813
2.1%
8 989
2.6%
9 1056
2.7%
10 1172
3.0%
11 1241
3.2%
ValueCountFrequency (%)
90 1
< 0.1%
87 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 2
< 0.1%
78 1
< 0.1%
77 1
< 0.1%
76 2
< 0.1%
75 2
< 0.1%
74 1
< 0.1%

int_rate_groups
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.3 KiB
9%-13%
14249 
13%-17%
10770 
5%-9%
9985 
17%-21%
3091 
21%-24%
 
482

Length

Max length7
Median length6
Mean length6.1129689
Min length5

Characters and Unicode

Total characters235820
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9%-13%
2nd row13%-17%
3rd row13%-17%
4th row13%-17%
5th row5%-9%

Common Values

ValueCountFrequency (%)
9%-13% 14249
36.9%
13%-17% 10770
27.9%
5%-9% 9985
25.9%
17%-21% 3091
 
8.0%
21%-24% 482
 
1.2%

Length

2023-05-10T23:30:00.834661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:30:01.092757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
9%-13 14249
36.9%
13%-17 10770
27.9%
5%-9 9985
25.9%
17%-21 3091
 
8.0%
21%-24 482
 
1.2%

Most occurring characters

ValueCountFrequency (%)
% 77154
32.7%
1 42453
18.0%
- 38577
16.4%
3 25019
 
10.6%
9 24234
 
10.3%
7 13861
 
5.9%
5 9985
 
4.2%
2 4055
 
1.7%
4 482
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120089
50.9%
Other Punctuation 77154
32.7%
Dash Punctuation 38577
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 42453
35.4%
3 25019
20.8%
9 24234
20.2%
7 13861
 
11.5%
5 9985
 
8.3%
2 4055
 
3.4%
4 482
 
0.4%
Other Punctuation
ValueCountFrequency (%)
% 77154
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 235820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
% 77154
32.7%
1 42453
18.0%
- 38577
16.4%
3 25019
 
10.6%
9 24234
 
10.3%
7 13861
 
5.9%
5 9985
 
4.2%
2 4055
 
1.7%
4 482
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 235820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 77154
32.7%
1 42453
18.0%
- 38577
16.4%
3 25019
 
10.6%
9 24234
 
10.3%
7 13861
 
5.9%
5 9985
 
4.2%
2 4055
 
1.7%
4 482
 
0.2%

open_acc_groups
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.3 KiB
2-10
25750 
10-19
11432 
19-27
 
1314
27-36
 
74
36-44
 
7

Length

Max length5
Median length4
Mean length4.3325038
Min length4

Characters and Unicode

Total characters167135
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-10
2nd row2-10
3rd row2-10
4th row2-10
5th row2-10

Common Values

ValueCountFrequency (%)
2-10 25750
66.7%
10-19 11432
29.6%
19-27 1314
 
3.4%
27-36 74
 
0.2%
36-44 7
 
< 0.1%

Length

2023-05-10T23:30:01.318032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:30:01.529420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2-10 25750
66.7%
10-19 11432
29.6%
19-27 1314
 
3.4%
27-36 74
 
0.2%
36-44 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 49928
29.9%
- 38577
23.1%
0 37182
22.2%
2 27138
16.2%
9 12746
 
7.6%
7 1388
 
0.8%
3 81
 
< 0.1%
6 81
 
< 0.1%
4 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128558
76.9%
Dash Punctuation 38577
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49928
38.8%
0 37182
28.9%
2 27138
21.1%
9 12746
 
9.9%
7 1388
 
1.1%
3 81
 
0.1%
6 81
 
0.1%
4 14
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 167135
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49928
29.9%
- 38577
23.1%
0 37182
22.2%
2 27138
16.2%
9 12746
 
7.6%
7 1388
 
0.8%
3 81
 
< 0.1%
6 81
 
< 0.1%
4 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 167135
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49928
29.9%
- 38577
23.1%
0 37182
22.2%
2 27138
16.2%
9 12746
 
7.6%
7 1388
 
0.8%
3 81
 
< 0.1%
6 81
 
< 0.1%
4 14
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.3 KiB
40-60
8402 
60-80
8012 
0-20
7765 
20-40
7715 
80-100
6683 

Length

Max length6
Median length5
Mean length4.9719522
Min length4

Characters and Unicode

Total characters191803
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80-100
2nd row0-20
3rd row80-100
4th row20-40
5th row20-40

Common Values

ValueCountFrequency (%)
40-60 8402
21.8%
60-80 8012
20.8%
0-20 7765
20.1%
20-40 7715
20.0%
80-100 6683
17.3%

Length

2023-05-10T23:30:01.743698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:30:01.975500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
40-60 8402
21.8%
60-80 8012
20.8%
0-20 7765
20.1%
20-40 7715
20.0%
80-100 6683
17.3%

Most occurring characters

ValueCountFrequency (%)
0 83837
43.7%
- 38577
20.1%
6 16414
 
8.6%
4 16117
 
8.4%
2 15480
 
8.1%
8 14695
 
7.7%
1 6683
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 153226
79.9%
Dash Punctuation 38577
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83837
54.7%
6 16414
 
10.7%
4 16117
 
10.5%
2 15480
 
10.1%
8 14695
 
9.6%
1 6683
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 191803
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83837
43.7%
- 38577
20.1%
6 16414
 
8.6%
4 16117
 
8.4%
2 15480
 
8.1%
8 14695
 
7.7%
1 6683
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 191803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83837
43.7%
- 38577
20.1%
6 16414
 
8.6%
4 16117
 
8.4%
2 15480
 
8.1%
8 14695
 
7.7%
1 6683
 
3.5%

total_acc_groups
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.3 KiB
2-20
18081 
20-37
16601 
37-55
3446 
55-74
 
435
74-90
 
14

Length

Max length5
Median length5
Mean length4.531301
Min length4

Characters and Unicode

Total characters174804
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2-20
2nd row2-20
3rd row2-20
4th row20-37
5th row2-20

Common Values

ValueCountFrequency (%)
2-20 18081
46.9%
20-37 16601
43.0%
37-55 3446
 
8.9%
55-74 435
 
1.1%
74-90 14
 
< 0.1%

Length

2023-05-10T23:30:02.188862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:30:02.402092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2-20 18081
46.9%
20-37 16601
43.0%
37-55 3446
 
8.9%
55-74 435
 
1.1%
74-90 14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 52763
30.2%
- 38577
22.1%
0 34696
19.8%
7 20496
 
11.7%
3 20047
 
11.5%
5 7762
 
4.4%
4 449
 
0.3%
9 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136227
77.9%
Dash Punctuation 38577
 
22.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52763
38.7%
0 34696
25.5%
7 20496
 
15.0%
3 20047
 
14.7%
5 7762
 
5.7%
4 449
 
0.3%
9 14
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 174804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52763
30.2%
- 38577
22.1%
0 34696
19.8%
7 20496
 
11.7%
3 20047
 
11.5%
5 7762
 
4.4%
4 449
 
0.3%
9 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52763
30.2%
- 38577
22.1%
0 34696
19.8%
7 20496
 
11.7%
3 20047
 
11.5%
5 7762
 
4.4%
4 449
 
0.3%
9 14
 
< 0.1%

annual_inc_groups
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.3 KiB
3k-31k
38569 
31k-58k
 
6
85k-112k
 
1
112k-140k
 
1

Length

Max length9
Median length6
Mean length6.0002851
Min length6

Characters and Unicode

Total characters231473
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row3k-31k
2nd row3k-31k
3rd row3k-31k
4th row3k-31k
5th row3k-31k

Common Values

ValueCountFrequency (%)
3k-31k 38569
> 99.9%
31k-58k 6
 
< 0.1%
85k-112k 1
 
< 0.1%
112k-140k 1
 
< 0.1%

Length

2023-05-10T23:30:02.605216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-10T23:30:02.823562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
3k-31k 38569
> 99.9%
31k-58k 6
 
< 0.1%
85k-112k 1
 
< 0.1%
112k-140k 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
k 77154
33.3%
3 77144
33.3%
1 38580
16.7%
- 38577
16.7%
5 7
 
< 0.1%
8 7
 
< 0.1%
2 2
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115742
50.0%
Lowercase Letter 77154
33.3%
Dash Punctuation 38577
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 77144
66.7%
1 38580
33.3%
5 7
 
< 0.1%
8 7
 
< 0.1%
2 2
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
k 77154
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 154319
66.7%
Latin 77154
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 77144
50.0%
1 38580
25.0%
- 38577
25.0%
5 7
 
< 0.1%
8 7
 
< 0.1%
2 2
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%
Latin
ValueCountFrequency (%)
k 77154
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 77154
33.3%
3 77144
33.3%
1 38580
16.7%
- 38577
16.7%
5 7
 
< 0.1%
8 7
 
< 0.1%
2 2
 
< 0.1%
4 1
 
< 0.1%
0 1
 
< 0.1%

Interactions

2023-05-10T23:29:46.306617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:22.364726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:25.092402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:27.613204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:30.181654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:32.791030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:35.390745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:37.790251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:40.339852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:43.397679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:46.559879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:22.693326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:25.346860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:27.869269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:30.442088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:33.036675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:35.635792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:38.034829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:40.604154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:43.691771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:46.822289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:22.944708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:25.601425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:28.127092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:30.693414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:33.343915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:35.886248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:38.284140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:40.902870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:43.992083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:47.091230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:23.255702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:25.870274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:28.399693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:30.951036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:33.602993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:36.139490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:38.543900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:41.409664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:44.299284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:47.330357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:23.503871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:26.127407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:28.678979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:31.192837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:33.914773image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:36.380063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:38.785602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:41.677583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:44.600296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:47.589519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:23.752857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:26.378399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:28.927759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:31.450819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:34.181342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:36.615345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:39.046644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:41.957405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:44.895078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:47.843347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:23.994950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:26.625608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:29.181629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:31.697042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:34.417492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:36.857892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:39.322383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:42.264669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:45.156858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:48.094990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:24.254344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:26.882965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:29.444811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:31.932259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:34.692324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:37.126066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:39.588274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:42.590126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:45.500768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:48.333138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:24.489632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:27.129654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:29.690090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:32.320585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:34.940995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:37.349291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:39.828384image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:42.870292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:45.831688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:48.573038image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:24.725315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:27.381044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:29.932145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:32.564344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:35.169451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:37.576193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:40.083906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:43.150472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-05-10T23:29:46.080177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-05-10T23:30:03.142791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
loan_amntfunded_amnt_invint_rateinstallmentannual_incdtiinq_last_6mthsopen_accrevol_utiltotal_acctermgradesub_gradeemp_lengthhome_ownershipverification_statusissue_dloan_statuspurposepub_recint_rate_groupsopen_acc_groupsrevol_util_groupstotal_acc_groupsannual_inc_groups
loan_amnt1.0000.9320.2440.9590.4270.070-0.0010.2080.0700.2740.3490.1360.1240.0460.0860.3070.0800.0660.1160.0220.1730.0720.0540.1170.001
funded_amnt_inv0.9321.0000.2430.9090.4000.079-0.0160.1910.0790.2610.3490.1290.1150.0510.0820.3080.1480.0540.1080.0230.1690.0660.0560.1110.000
int_rate0.2440.2431.0000.2440.0540.1180.166-0.0320.467-0.0750.4690.7120.6810.0260.0570.1580.1600.2120.0600.0540.9640.0760.2380.0550.000
installment0.9590.9090.2441.0000.4190.0640.0010.1980.1010.2470.1340.1350.1220.0380.0680.2670.0760.0390.1130.0210.1650.0690.0640.1060.000
annual_inc0.4270.4000.0540.4191.000-0.1020.0350.3060.0480.4320.0000.0000.0000.0040.0000.0020.0190.0000.0000.0000.0000.0000.0060.0001.000
dti0.0700.0790.1180.064-0.1021.0000.0140.3050.2740.2390.0770.0630.0570.0170.0250.0700.0430.0470.0820.0150.0760.1140.1700.1050.021
inq_last_6mths-0.001-0.0160.1660.0010.0350.0141.0000.096-0.0480.1070.0480.0780.0840.0030.0420.0120.0380.0750.0400.0210.0940.0440.0540.0540.000
open_acc0.2080.191-0.0320.1980.3060.3050.0961.000-0.0800.6890.0490.0770.0850.0290.1070.0710.0250.0100.0470.0000.0941.0000.0820.3610.000
revol_util0.0700.0790.4670.1010.0480.274-0.048-0.0801.000-0.0630.0690.2050.1780.0060.0520.0460.0470.0990.1010.0320.2400.0731.0000.0510.005
total_acc0.2740.261-0.0750.2470.4320.2390.1070.689-0.0631.0000.0980.0510.0510.0690.1690.1000.0290.0310.0480.0230.0630.3530.0511.0000.000
term0.3490.3490.4690.1340.0000.0770.0480.0490.0690.0981.0000.4410.4750.1000.1040.2480.3430.1730.1120.0130.4520.0410.0680.0870.004
grade0.1360.1290.7120.1350.0000.0630.0780.0770.2050.0510.4411.0001.0000.0180.0510.1370.0700.2020.0690.0550.7790.0710.2400.0500.000
sub_grade0.1240.1150.6810.1220.0000.0570.0840.0850.1780.0510.4751.0001.0000.0220.0570.1450.0430.2070.0530.0580.8250.0990.2540.0620.000
emp_length0.0460.0510.0260.0380.0040.0170.0030.0290.0060.0690.1000.0180.0221.0000.1290.0830.0620.0280.0350.0370.0300.0350.0120.0890.000
home_ownership0.0860.0820.0570.0680.0000.0250.0420.1070.0520.1690.1040.0510.0570.1291.0000.0740.1330.0220.1250.0130.0510.0820.0510.1540.000
verification_status0.3070.3080.1580.2670.0020.0700.0120.0710.0460.1000.2480.1370.1450.0830.0741.0000.2880.0480.0950.0080.1500.0580.0450.0910.000
issue_d0.0800.1480.1600.0760.0190.0430.0380.0250.0470.0290.3430.0700.0430.0620.1330.2881.0000.0640.0730.0200.1510.0230.0680.0330.000
loan_status0.0660.0540.2120.0390.0000.0470.0750.0100.0990.0310.1730.2020.2070.0280.0220.0480.0641.0000.0970.0540.2040.0090.0970.0240.000
purpose0.1160.1080.0600.1130.0000.0820.0400.0470.1010.0480.1120.0690.0530.0350.1250.0950.0730.0971.0000.0170.0810.0490.1450.0550.000
pub_rec0.0220.0230.0540.0210.0000.0150.0210.0000.0320.0230.0130.0550.0580.0370.0130.0080.0200.0540.0171.0000.0520.0000.0300.0150.000
int_rate_groups0.1730.1690.9640.1650.0000.0760.0940.0940.2400.0630.4520.7790.8250.0300.0510.1500.1510.2040.0810.0521.0000.0770.2300.0520.000
open_acc_groups0.0720.0660.0760.0690.0000.1140.0441.0000.0730.3530.0410.0710.0990.0350.0820.0580.0230.0090.0490.0000.0771.0000.0670.3250.000
revol_util_groups0.0540.0560.2380.0640.0060.1700.0540.0821.0000.0510.0680.2400.2540.0120.0510.0450.0680.0970.1450.0300.2300.0671.0000.0460.001
total_acc_groups0.1170.1110.0550.1060.0000.1050.0540.3610.0511.0000.0870.0500.0620.0890.1540.0910.0330.0240.0550.0150.0520.3250.0461.0000.000
annual_inc_groups0.0010.0000.0000.0001.0000.0210.0000.0000.0050.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0001.000

Missing values

2023-05-10T23:29:49.084766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-10T23:29:50.351555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

loan_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposedtiearliest_cr_lineinq_last_6mthsopen_accpub_recrevol_utiltotal_accint_rate_groupsopen_acc_groupsrevol_util_groupstotal_acc_groupsannual_inc_groups
050004975.036 months10.65162.87BB210+ yearsRENT24000.0VerifiedDec-11Fully Paidcredit_card27.65Jan-8513.0083.79.09%-13%2-1080-1002-203k-31k
125002500.060 months15.2759.83CC4< 1 yearRENT30000.0Source VerifiedDec-11Charged Offcar1.00Apr-9953.009.44.013%-17%2-100-202-203k-31k
224002400.036 months15.9684.33CC510+ yearsRENT12252.0Not VerifiedDec-11Fully Paidsmall_business8.72Nov-0122.0098.510.013%-17%2-1080-1002-203k-31k
31000010000.036 months13.49339.31CC110+ yearsRENT49200.0Source VerifiedDec-11Fully Paidother20.00Feb-96110.0021.037.013%-17%2-1020-4020-373k-31k
550005000.036 months7.90156.46AA43 yearsRENT36000.0Source VerifiedDec-11Fully Paidwedding11.20Nov-0439.0028.312.05%-9%2-1020-402-203k-31k
670007000.060 months15.96170.08CC58 yearsRENT47004.0Not VerifiedDec-11Fully Paiddebt_consolidation23.51Jul-0517.0085.611.013%-17%2-1080-1002-203k-31k
730003000.036 months18.64109.43EE19 yearsRENT48000.0Source VerifiedDec-11Fully Paidcar5.35Jan-0724.0087.54.017%-21%2-1080-1002-203k-31k
856005600.060 months21.28152.39FF24 yearsOWN40000.0Source VerifiedDec-11Charged Offsmall_business5.55Apr-04211.0032.613.021%-24%10-1920-402-203k-31k
953755350.060 months12.69121.45BB5< 1 yearRENT15000.0VerifiedDec-11Charged Offother18.08Sep-0402.0036.53.09%-13%2-1020-402-203k-31k
1065006500.060 months14.65153.45CC35 yearsOWN72000.0Not VerifiedDec-11Fully Paiddebt_consolidation16.12Jan-98214.0020.623.013%-17%10-1920-4020-373k-31k
loan_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspurposedtiearliest_cr_lineinq_last_6mthsopen_accpub_recrevol_utiltotal_accint_rate_groupsopen_acc_groupsrevol_util_groupstotal_acc_groupsannual_inc_groups
397075000525.036 months9.33159.77BB32 yearsMORTGAGE180000.0Not VerifiedJul-07Fully Paidhome_improvement11.93Feb-95116.0039.238.09%-13%10-1920-4037-553k-31k
397085000375.036 months9.96161.25BB54 yearsMORTGAGE48000.0Not VerifiedJul-07Fully Paiddebt_consolidation8.03Aug-9516.0048.66.09%-13%2-1040-602-203k-31k
397095000675.036 months11.22164.23CC4< 1 yearOWN80000.0Not VerifiedJul-07Fully Paidcredit_card1.21Jul-96315.0116.129.09%-13%10-190-2020-373k-31k
397105000250.036 months7.43155.38AA21 yearOWN85000.0Not VerifiedJul-07Fully Paidcredit_card0.31Oct-9707.000.619.05%-9%2-100-202-203k-31k
397115000700.036 months8.70158.30BB15 yearsMORTGAGE75000.0Not VerifiedJul-07Fully Paidcredit_card15.55May-94010.0023.029.05%-9%2-1020-4020-373k-31k
3971225001075.036 months8.0778.42AA44 yearsMORTGAGE110000.0Not VerifiedJul-07Fully Paidhome_improvement11.33Nov-90013.0013.140.05%-9%10-190-2037-553k-31k
397138500875.036 months10.28275.38CC13 yearsRENT18000.0Not VerifiedJul-07Fully Paidcredit_card6.40Dec-8616.0026.99.09%-13%2-1020-402-203k-31k
3971450001325.036 months8.07156.84AA4< 1 yearMORTGAGE100000.0Not VerifiedJul-07Fully Paiddebt_consolidation2.30Oct-98011.0019.420.05%-9%10-190-2020-373k-31k
397155000650.036 months7.43155.38AA2< 1 yearMORTGAGE200000.0Not VerifiedJul-07Fully Paidother3.72Nov-88017.000.726.05%-9%10-190-2020-373k-31k
397167500800.036 months13.75255.43EE2< 1 yearOWN22000.0Not VerifiedJun-07Fully Paiddebt_consolidation14.29Oct-0307.0051.58.013%-17%2-1040-602-203k-31k